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1,744 results on '"Yang, Dong"'

137. Transient kinetics reveal the mechanism of competitive inhibition of the neutral amino acid transporter ASCT2.

Author

Yang Dong, Jiali Wang, and Grewer, Christof

Subjects
*FOREIGN exchange rates, *AMINO acids, *HYDROXYPROLINE, *CELL membranes, *BIOCHEMICAL substrates, *ALANINE
Abstract

ASCT2 (alanine serine cysteine transporter 2), a member of the solute carrier 1 family, mediates Na+-dependent exchange of small neutral amino acids across cell membranes. ASCT2 was shown to be highly expressed in tumor cells, making it a promising target for anticancer therapies. In this study, we explored the binding mechanism of the high-affinity competitive inhibitor L-cis hydroxyproline biphenyl ester (Lc-BPE) with ASCT2, using electrophysiological and rapid kinetic methods. Our investigations reveal that Lc-BPE binding requires one or two Na+ ions initially bound to the apo-transporter with high affinity, with Na1 site occupancy being more critical for inhibitor binding. In contrast to the amino acid substrate bound form, the final, third Na+ ion cannot bind, due to distortion of its binding site (Na2), thus preventing the formation of a translocation-competent complex. Based on the rapid kinetic analysis, the application of Lc-BPE generated outward transient currents, indicating that despite its net neutral nature, the binding of Lc-BPE in ASCT2 is weakly electrogenic, most likely because of asymmetric charge distribution within the amino acid moiety of the inhibitor. The preincubation with Lc-BPE also led to a decrease of the turnover rate of substrate exchange and a delay in the activation of substrate-induced anion current, indicating relatively slow Lc-BPE dissociation kinetics. Overall, our results provide new insight into the mechanism of binding of a prototypical competitive inhibitor to the ASCT transporters. [ABSTRACT FROM AUTHOR]

Published
2024
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138. Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.

Author

Belue, Mason J., Harmon, Stephanie A., Yang, Dong, An, Julie Y., Gaur, Sonia, Law, Yan Mee, Turkbey, Evrim, Xu, Ziyue, Tetreault, Jesse, Lay, Nathan S., Yilmaz, Enis C., Phelps, Tim E., Simon, Benjamin, Lindenberg, Liza, Mena, Esther, Pinto, Peter A., Bagci, Ulas, Wood, Bradford J., Citrin, Deborah E., and Dahut, William L.

Abstract

Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists. This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36). In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1–31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0–0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0–3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%). Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa. [ABSTRACT FROM AUTHOR]

Published
2024
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148. VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images

Author

Sekuboyina, Anjany, Husseini, Malek E., Bayat, Amirhossein, Löffler, Maximilian, Liebl, Hans, Li, Hongwei, Tetteh, Giles, Kukačka, Jan, Payer, Christian, Štern, Darko, Urschler, Martin, Chen, Maodong, Cheng, Dalong, Lessmann, Nikolas, Hu, Yujin, Wang, Tianfu, Yang, Dong, Xu, Daguang, Ambellan, Felix, Amiranashvili, Tamaz, Ehlke, Moritz, Lamecker, Hans, Lehnert, Sebastian, Lirio, Marilia, Olaguer, Nicolás Pérez de, Ramm, Heiko, Sahu, Manish, Tack, Alexander, Zachow, Stefan, Jiang, Tao, Ma, Xinjun, Angerman, Christoph, Wang, Xin, Brown, Kevin, Kirszenberg, Alexandre, Puybareau, Élodie, Chen, Di, Bai, Yiwei, Rapazzo, Brandon H., Yeah, Timyoas, Zhang, Amber, Xu, Shangliang, Hou, Feng, He, Zhiqiang, Zeng, Chan, Xiangshang, Zheng, Liming, Xu, Netherton, Tucker J., Mumme, Raymond P., Court, Laurence E., Huang, Zixun, He, Chenhang, Wang, Li-Wen, Ling, Sai Ho, Huỳnh, Lê Duy, Boutry, Nicolas, Jakubicek, Roman, Chmelik, Jiri, Mulay, Supriti, Sivaprakasam, Mohanasankar, Paetzold, Johannes C., Shit, Suprosanna, Ezhov, Ivan, Wiestler, Benedikt, Glocker, Ben, Valentinitsch, Alexander, Rempfler, Markus, Menze, Björn H., and Kirschke, Jan S.

Published
2021
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